Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring

Abstract

Structural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is aging and approaching the end of its design working life. The detection of damage is usually performed through artificial intelligence techniques. In contrast, tools for the localization and the estimation of the extent of the damage are limited, mainly due to the complete datasets of damages needed for training the system. The proposed approach consists in numerically generating datasets of damaged structures on the basis of random variables representing the actions and the possible damages. Neural networks were trained to perform the main structural monitoring tasks: damage detection, localization, and estimation. The artificial intelligence tool interpreted the measurements on a real structure. To simulate real measurements more accurately, noise was added to the synthetic dataset. The results indicate that the accuracy of the measurement devices plays a relevant role in the quality of the monitoring.

Innovative Methods and Materials in Structural Health Monitoring of Civil Infrastructures

“…The Structural Health Monitoring Systems is performed with the Artificial Intelligence by Chiaia and De Biagi [22], while Ozer and Feng propose the Participatory Sensing and Mobile Cyber-Physical [23].…”